Business performance assessment of small and medium-sized enterprises: Evidence from the Czech Republic

• Main Authors: Vojtech Stehel, Jakub Horak, Tomas Krulicky
• Format: Article
• Language: English
• Published: LLC "CPC "Business Perspectives" 2021-09-01
• Series: Problems and Perspectives in Management
• Subjects: enterprise management; Kohonen neural networks (KNN); neural networks; principal component analysis (PCA)
• Online Access: https://www.businessperspectives.org/images/pdf/applications/publishing/templates/article/assets/15584/PPM_2021_03_Stehel.pdf

Business performance assessment is one of the basic tasks of management. Business performance can be assessed using a number of methods. The basic ones include financial analysis, Balanced Scorecard or Economic Value Added (EVA). The paper is focused on SME business performance assessment based on Economic Value Added, calculated using the INFA build-up model. According to this method, companies were divided into four categories. The first category included companies with a positive EVA value. The second category included companies with negative EVA, but with the economic result above the risk-free rate. The third category included companies with a positive economic result above the risk-free rate. The fourth category included companies with a negative economic result. The model did not include companies with negative equity. The input represented 15 predictors based on their financial statements. The data were normalized and all extreme values, likely caused by a data rewriting error, were removed. Company performance is visualized by comparing Principal Component Analysis and Kohonen neural networks. Compared to similar research, the methods are compared using the data that analyzes the performance of companies. Both methods made it possible to visualize the given task. With regard to the purpose of facilitating the interpretation of the results, for the given case, the use of PC seems to be more appropriate. AcknowledgmentThis study has been supported by the Technology Agency of the Czech Republic under project No TL01000349.